Pattern inspection device and pattern inspection method

ABSTRACT

Provided is a pattern inspection device for accurately simulating an electron beam image of a circuit pattern on a wafer from design data, and implementing high-precision defect detection based on the comparison between the simulated electron beam image and a real image. A pattern inspection device comprises: an image capturing unit for capturing an electron beam image of a pattern formed on a substrate; a simulated electron beam image generation unit for generating a simulated electron beam image using a parameter indicating the characteristics of the electron beam image on the basis of design data; and an inspection unit for comparing the electron beam image of the pattern, which is the image captured by the image capturing unit, and the simulated electron beam image generated by the simulated electron beam image generation unit, and inspecting the pattern on the substrate.

TECHNICAL FIELD

The present invention relates to a pattern inspection device and apattern inspection method.

BACKGROUND ART

As a semiconductor circuit pattern becomes more and more miniaturized,the resolution of an exposure device is approaching the limit, making itmore and more difficult to form a pattern on a wafer as designed. Thegeneration frequency of systematic defects, such as a line widthdeviation from a design value or a deformed tip shape, increases.Because these systematic defects are generated in any of all dies, it isdifficult to detect them in the conventional die-to-die (or abbreviatedto D2D) inspection that compares between the data on the neighboringdies. Therefore, there is an increased need for the die-to-database (orabbreviated to D2DB) inspection that compares data on a die with designdata.

For a comparison between various types of data such as between a realimage and design data, JP-A-2006-11270 (Patent Literature 1) discloses“a method for generating an image, which is generated from design databy simulating an imaged real image, for use as a reference image”.

CITATION LIST Patent Literature PATENT LITERATURE 1: JP-A-2006-11270SUMMARY OF INVENTION Technical Problem

Because the technology disclosed in Patent Literature 1 assumes anoptical inspection for an exposure mask, the simulation method for areal image assumes the simulation of the optical image of an exposuremask. This literature describes in detail the method for reflecting theblur of an edge of an optical image onto a simulated image. However, theproblem is that no consideration is paid for the bright differencebetween a pattern and a background and for the brightness difference inthe edge direction; these brightness differences are the problemsgenerated when inspecting a resist pattern on a wafer with the use of anelectron beam type inspection device. Another problem is that noapplication is provided deriving various parameters that are necessaryfor generating a simulated image and that represent the characteristicsof a real image.

It is an object of the present invention to provide a pattern inspectiondevice and a pattern inspection method for accurately simulating theelectron beam image of a resist pattern on a wafer from design data andfor implementing high-accuracy defect detection by comparing thesimulated data and the real image.

Solution to Problem

To solve the above problems, the configuration described in CLAIMS isused. The present application includes a plurality of means for solvingthe problems described above. One of them is as follows.

A pattern inspection device includes an imaging unit that images anelectron beam image of a pattern formed on a substrate; a simulatedelectron beam image generation unit that generates a simulated electronbeam image using a parameter based on design data, the parameterrepresenting a characteristic of the electron beam image; and aninspection unit that inspects the pattern on the substrate by comparingthe electron beam image of the pattern and the simulated electron beamimage, the electron beam image of the pattern being imaged by saidimaging unit, the simulated electron beam image being generated by saidsimulated electron beam image generation unit.

Advantageous Effects of Invention

According to the present invention, there is provided a patterninspection device and a pattern inspection method for accuratelysimulating the electron beam image of a resist pattern on a wafer fromdesign data and for implementing high-accuracy defect detection bycomparing the simulated data and the real image.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram showing an overall flow in a first embodiment of thepresent invention.

FIG. 2 is a diagram showing a system configuration in the firstembodiment of the present invention.

FIG. 3 is a diagram showing a flow of shade comparison in the firstembodiment of the present invention.

FIG. 4 is a diagram showing a method for detecting a positionaldeviation in the first embodiment of the present invention.

FIG. 5 is a diagram showing the characteristic of a SEM image in thefirst embodiment of the present invention.

FIG. 6 is a diagram showing a method for modeling a SEM image in thefirst embodiment of the present invention.

FIG. 7 is a diagram showing a method for calculating parameters forgenerating a simulated SEM image in the first embodiment of the presentinvention.

FIG. 8 is a diagram showing a method for calculating parameters forgenerating a simulated SEM image in the first embodiment of the presentinvention.

FIG. 9 is a diagram showing a method for calculating parameters forgenerating a simulated SEM image in the first embodiment of the presentinvention.

FIG. 10 is a diagram showing a flow of simulated SEM image generationprocessing in the first embodiment of the present invention.

FIG. 11 is a diagram showing an intermediate processing result in thesimulated SEM image generation in the first embodiment of the presentinvention.

FIG. 12 is a diagram showing a method for calculating an edge directionin the simulated SEM image generation in the first embodiment of thepresent invention.

FIG. 13 is a diagram showing an example of a GUI screen for settingsimulated SEM image generation parameters in the first embodiment of thepresent invention.

FIG. 14 is a diagram showing another example of a GUI screen for settingsimulated SEM image generation parameters in the first embodiment of thepresent invention.

FIG. 15 is a diagram showing a still another example of a GUI screen forsetting simulated SEM image generation parameters in the firstembodiment of the present invention.

FIG. 16 is a diagram showing an example of a GUI screen for calculatingsimulated SEM image generation parameters in the first embodiment of thepresent invention.

FIG. 17 is a diagram showing a variation in design data in a secondembodiment of the embodiment of the present invention.

FIG. 18 is a diagram showing a design data conversion method when adesign intent is used in the second embodiment of the present invention.

FIG. 19 is a diagram showing a design data conversion method when maskpattern data is used in the second embodiment of the present invention.

FIG. 20 is a diagram showing a flow of simulated SEM image generationprocessing when mask pattern data is used in the second embodiment ofthe present invention.

FIG. 21 is a diagram showing a flow of shade comparison, which allowsfor a process variation, in a third embodiment of the present invention.

FIG. 22 is a diagram showing a flow of shade comparison, which allowsfor a process variation, in a fourth embodiment of the presentinvention.

FIG. 23 is a diagram showing a method for modeling a SEM image otherthan a resist pattern in a fifth embodiment of the present invention.

FIG. 24 is a diagram showing a method for modeling a SEM imageconsidering pattern density in a sixth embodiment of the presentinvention.

FIG. 25 is a diagram showing an overall flow of a method for latercalculating parameters for simulated SEM image generation in a seventhembodiment of the present invention.

FIG. 26 is a diagram showing a flow of inspection, in which brightnesscorrection is included, in an eighth embodiment of the presentinvention.

FIG. 27 is a diagram showing a method for correcting brightness in theeighth embodiment of the present invention.

FIG. 28 is a diagram showing another calculation method for brightnesscorrection coefficient in a ninth embodiment of the present invention.

FIG. 29 is a diagram showing a flow of fixed-point inspection in a tenthembodiment of the present invention.

FIG. 30 is a diagram showing the evaluation values output as aninspection result of the fixed-point inspection in the tenth embodimentof the present invention.

FIG. 31 is a diagram showing a method for calculating defectdetermination values in an eleventh embodiment of the present invention.

FIG. 32 is a diagram showing a method for using a simulated SEM image indetecting a positional deviation in a twelfth embodiment of the presentinvention.

FIG. 33 is a diagram showing a variation in electron beam scanning in athirteenth embodiment of the present invention.

FIG. 34 is a diagram showing a multi-beam type optical system in afourteenth embodiment of the present invention.

DESCRIPTION OF EMBODIMENTS First Embodiment

The following describes an overall configuration of a first embodimentof the present invention, followed sequentially by the description ofthe content of each processing.

(1-1) Overall Flow

First, with reference to FIG. 2, the overall configuration of a patterninspection device according to the present invention is described.

In this embodiment, a wafer image to be inspected is acquired by ascanning electron microscope (SEM). An electron optics system 100includes an electron source 101 that generates an electron beam, acondenser lens 102 that converges the electron beam, a deflector 103that deflects the electron beam into the XY direction, an object lens104, and an XY stage 105. A secondary electron 107 generated from awafer 106 is detected by a detector 108, is converted from the analogsignal to the digital signal by an A/D converter 109, is input to animage input unit 110, and is stored in a storage device 111.

An image to be inspected is one of the two types of image: continuousimage and sheet image. A continuous image is obtained by theone-dimensional scanning of an electron beam and the continuous movementof the stage. A sheet image is obtained by the two-dimensional scanningof an electron beam and the step movement of the stage.

A sequence of inspection processing is performed by a system managementunit 115 via a bus 114. Before the inspection, design data is stored inadvance in a storage device 112. A design data calculation unit 113converts the format of design data and selects a required portion, andan image processing unit 116 compares the design data and theabove-mentioned inspection image for defect determination. Theinspection result is output to a result output unit 117 and is stored inan inspection result storage unit 118.

FIG. 1 shows an overall flow. FIG. 1( a) shows a flow of conditionsetting performed before the inspection, and FIG. 1( b) shows a flow ofinspection. In this embodiment, the design data and an inspection imageare compared for defect determination as described above; in moredetail, a simulated SEM image, which simulates the inspection image, isgenerated from design data, and this simulated SEM image is comparedwith the inspection image (real SEM image) for detecting a defect.

Before the inspection, various parameters, necessary for generating asimulated SEM image, are set during condition setting. First, the designdata is read (S510), and the acquisition position on the real SEM image,used to calculate various parameters (pattern, background, edgebrightness, blur amount of edges) necessary for generating the simulatedSEM image, is determined on the design data (S511). After that, the realSEM image at that position is acquired (S512), the above parameters arecalculated using a predetermined calculation method, and the calculatedparameters are written in the condition file (S513). In the descriptionbelow, this condition file is called a “model condition file” todistinguish this file from other files. The detail of step S511 and S512will be described later.

At inspection time, the model condition file is read first (S520) andthen the design data on a position, corresponding to an inspection area,is read (S521). After that, the real SEM image is acquired (S522) and apositional deviation between the design data and the real SEM image isdetected (S523) and, based on the result, the design data is transformedand extracted (S524). Next, the simulated SEM image generationparameters, stored in the model condition file, are applied to thetransformed/extracted design data to generate a simulated SEM image(S525). The obtained simulated SEM image and the real SEM image arecompared (S526) for defect determination, and the coordinates of thedefect portion and the defect size are output (S527). The steps fromS521 to S527 are repeated until the inspection area is ended.

(1-2) Defect Determination Method

The defect determination flow is described below with reference to FIG.3 though a part of the description overlaps with the description of FIG.1( b) given above. This flow is executed by the image processing unit116 in FIG. 1.

First, the SEM image stored in the storage device 111 in FIG. 1 and thedesign data stored in the storage device 112 are read into the imageprocessing unit 116 via the bus 114 (S202, S201). At this time,considering a deviation in the image acquisition position of the SEMimage, design data 204 on an area larger than a SEM image 203 is read.

Next, the positional deviation between the design data and the SEM imageis detected (S205). The specific method for detecting a positionaldeviation will be described in (1-3). Because the SEM image includes animage distortion caused by the scanning distortion of the electron beamor by a vibration in the stage, the positional deviation amount is notuniform in the image but is different among the points in the image.Therefore, the output is a positional deviation map 206 that indicates adeviation amount (Δx, Δy) for each position (x, y). After the designdata is transformed based on the positional deviation map 206, the areacorresponding to the SEM image is extracted (S207). After that, asimulated SEM image 209, which simulates the SEM image, is generatedfrom the selected design data (S208). The method for generating asimulated SEM image will be described in (1-4), (1-5), (1-6), (1-7), and(1-8). A defect is determined by comparing the simulated SEM image 209and the SEM image 203 (S210). Basically, a portion, where the shadedifference between the two is higher than a predetermined threshold THas indicated by expression (1), is determined as a defect, where thereal SEM image is represented by f(x, y) and the simulated SEM image byg(x, y).

(MATH. 1)

|f(x,y)−g(x,y)|>TH  (1)

According to this embodiment, because comparison is performed using anon-defective simulated SEM image, a defect can be detected even if itis generated in any of the dies and cannot be detected by thetraditional D2D inspection. In addition, in the D2D inspection, thepost-processing is required to determine whether the defect is includedin the inspection target f(x, y) or in the reference image g(x, y). Suchpost-processing is not required in this embodiment. In addition, adefect is determined based on the shade difference in this embodiment.Therefore, not only a defect caused by a change in the geometrical shapebut also a defect generated due to a difference in image brightness,such as the one caused by a remnant resist film, can be detected.

Various technologies for increasing the inspection performance ofshaded-image comparison may also be applied. These technologies includethe local perturbation method, which is a method for detecting a defectthrough alignment on a local area basis, and the method that allows fora small shade difference caused by a positional deviation, equal to orsmaller than a pixel, between the images to be compared.

As a defect determination result, the position coordinates of adefective portion 211, as well as the characteristic amounts such as thedefect size or shade value of the defective portion, are output (S212).

(1-3) Positional Deviation Detection and Distortion Correction

Next, the method for detecting a positional deviation between the designdata 204 and the SEM image 203, which is executed in step S523 in FIG. 1or step S205 in FIG. 3, and distortion correction/extraction, which isexecuted in step S524 in FIG. 1 or step S207 in FIG. 3, are described.

Because the SEM image includes the scanning distortion of the electronbeam or an image distortion caused by a vibration in the stage asdescribed above, the positional deviation amount for the design data isnot uniform in the image but is different among the points in the image.To detect this positional deviation amount, the image is divided intosmall meshes as shown in FIG. 4( a) to calculate the positionaldeviation amount in each mesh, or the positional deviation amount iscalculated at a point (an area surrounded by a rectangle in the figure),such as a corner of a pattern shown in FIG. 4( b), where the positionaldeviation amount is determined uniquely. It is desirable that thepositional deviation detection points, such as those shown in FIG. 4(b), be automatically selected using the design data before theinspection.

The positional deviation amount between the SEM image and the designdata is detected as follows. First, a small area of the design data isrelatively moved to a small area of the SEM image (each mesh in FIG. 4(a) and an area surrounded by a small square in FIG. 4( b)) and then themovement amount that minimizes the sum of squares of the differencebetween the two is calculated. Instead of the sum of squares of thedifference, the movement amount, which maximizes the correlationcoefficient between the two, may also be calculated. The positionaldeviation amount is calculated at an equal interval in FIG. 4( a), andat an unequal interval in FIG. 4( b). In either case, the positionaldeviation amount is defined at the representative coordinate points inthe image. By interpolating the positional deviation amount at therepresentative coordinate points, the positional deviation amount at allcoordinates is calculated. The positional deviation map 206, shown inFIG. 3, includes the thus-calculated positional deviation amount (Δx,Δy) of the design data corresponding to each pixel (x, y) of the realSEM image. In step S207 in FIG. 3, the design data corresponding to eachpixel of the SEM image is selected according to the positional deviationmap. By performing the processing described above, the design data,which locally matches the real SEM image in position (that is, deformedsimilarly), can be obtained.

According to this embodiment, instead of correcting a distortion to makethe image match the design data, the design data is distorted to makethe design data match the image. This method prevents uneven imagedeteriorations that may be caused by distorting the image, thusproviding the advantage in detecting a defect.

(1-4) Modelling of Simulated SEM Image Generation

Next, the generation of the simulated SEM image, which is performed instep S525 in FIG. 1 or in step S208 in FIG. 3, is described. FIG. 5schematically shows the characteristic of a SEM image. FIG. 5( b) is across section taken along line A-B. As shown in the figure, the SEMimage of a resist pattern formed on a wafer is inspected in the firstembodiment. A SEM image has the following characteristic.

(1) The edge portion (303, 304, 305) is brighter than the flat portion(301, 302). This is due to a tilt angle effect or an edge effect; thisis a general characteristic of a secondary electron image.(2) In general, the pattern portion (302) and the background portion(301) are different in brightness. The brightness of each portiondepends on the material or the imaging condition.(3) The brightness of an edge portion depends on the direction of theedge. This is the effect of the charging state of a sample. In manycases, an edge parallel to the scanning direction of the electron beamis darker than an edge vertical to the scanning direction as shown inFIG. 5( a). (In this example in which a scanning direction 306 of theelectron beam is the horizontal direction, a horizontal-direction edge305 is darker than a vertical-direction edge 303. An oblique edge 304 isintermediate in brightness).

To generate a simulated SEM image, on which the above-describedcharacteristic of the SEM image is reflected, from the design data, aSEM image is modeled as shown in FIG. 6. The signal intensities e, b,and p are given to the design data, which is the line drawing (vectordata) such as the one shown by the reference numeral 204 in FIG. 3, inthe order of the signal intensity of the edge portion, backgroundportion, and pattern portion as shown in 310. In more detail, toreproduce the characteristic described in (3) above, the signalintensity ev is given to the vertical-direction edge, and the signalintensity eh to the horizontal-direction edge. In addition, the edgewidth w is given. The blur of the edge portion, similar to that of thereal image, is reproduced by convoluting a blur function 311,corresponding to the illumination beam intensity distribution, into asignal 310 (312). For example, the Gaussian function is used as thefunction representing the beam intensity distribution. In the modelingdescribed above, the SEM image is simulated by the six parameters, thatis, the signal intensities ev, eh, b, and p, edge width w, and beam sizeσ. It is desirable that these parameters be determined before theinspection such that they match the real SEM image. The specificcalculation method for the parameters will be described in (1-5) and thesubsequent parts.

The Monte Carlo simulation, which receives the cross section shape ofthe resist pattern as the input information, may also be used as amethod for generating a simulated SEM image. However, this method is notpractical when the inspection area is large because it requires a hugeamount of calculation time. The simulated SEM image generation methoddescribed above, simple and speedy in processing, can generate asimulated SEM image in synchronization with the input of an image evenin an inspection in which continuous images, obtained by the continuousmovement of the stage, are processed.

(1-5) Calculation Method of Simulated SEM Image Generation Parameters

As described above, the parameters necessary for generating a simulatedSEM image in this embodiment are the following six parameters: signalintensities ev, eh, b, and p, edge width w, and beam size cr. Thedetermination method for these parameters is described below one by one.

FIG. 7 shows the determination method for the edge width w. As shown inFIG. 7( a), the user may enter a value 450 itself, or may enter a filmthickness 451 and a taper angle 452, as the edge width. If there is aportion in the real SEM image where the edge width largely differs fromthe value, that portion is detected as a defect.

The signal intensity p of the pattern and the signal intensity b of thebackground are calculated from the real SEM image. FIG. 8 shows a partof the design data. The signal intensity p of the pattern is the averagevalue of the brightness of the real SEM image corresponding to a widepattern area 401. The signal intensity b of the background is theaverage value of the brightness of the real SEM image corresponding to awide background area 402.

The signal intensities ev and eh of an edge portion, as well as the beamsize σ, are calculated from the real SEM image. To calculate thevertical-direction signal intensity ev, a real SEM image thatcorresponds to an area 403, in which the vertical edge continues for apredetermined length and the pattern size is relatively large, is used.To calculate the horizontal-direction edge intensity eh, a real SEMimage that corresponds to an area 404, in which the horizontal edgecontinues for a predetermined length and the pattern size is relativelylarge, is used.

FIG. 9 shows the calculation method for the signal intensity (ev or eh)of an edge portion and the beam size σ. In the description below, it isassumed that the values of p and b are already determined. Signalwaveforms 410, which include different signal intensities (e1-em) of theedge portion as indicated by the reference numeral 410, and (σ1-σn)Gaussian functions 411, which represent different a values, arecalculated in advance, and the functions 411 are convoluted into thesignal waveforms 410. Let model(x) be the result (412). There are m×nresults of model (x) in total. On the other hand, the real signalwaveform real (x) is obtained from a real SEM image 413 (correspondingto the area 403 in FIG. 8). After that, the signal intensity and thebeam size of the edge portion are determined such that the sum ofsquares of the differences between model(x) and real(x), calculated byexpression (2), is minimized (414).

(MATH. 2)

Σ{real(x)−model(x)}²  (2)

The same calculation method is used though ev uses the vertical edge ofthe real SEM image and eh uses the horizontal edge portion of the realSEM image. Instead using the round robin of a combination of n×m, it isalso possible to specify an initial value and then recursively performthe calculation using the steepest descent method, Gauss-Newton method,or Levenberg-Marquardt method.

Because the brightness of the real SEM image differs between thevertical edge and the horizontal edge as described in (1-4) above, it isimportant to set parameter values separately as described above. Inaddition to the vertical/horizontal edge, the real pattern includes anoblique edge. The processing for an oblique edge is described in (1-6).

(1-6) Image Processing Flow of Simulated SEM Image Generation

With reference to FIG. 10 to FIG. 12, the image processing flow of asimulated SEM image is described below. In this processing, thesimulated SEM image generation parameters (signal intensities ev, eh, b,and p, edge width w, and beam size σ.), determined in the descriptionabove, are used. The following describes the steps in FIG. 10, one stepat a time.

[Steps in FIG. 10]

S601: Receive the design data.S602: Sample the data with a predetermined pixel size and create abinary image where the pattern portion is represented by 1 and thebackground portion by 0 (FIG. 11( a)).S603: Detect the edge of the binary image (FIG. 11( b)).S604: Calculate the direction of each edge point. The specific methodfor calculating the edge direction is described with reference to FIG.12. The reference numeral 701 indicates the image after the edge isdetected in step S603, and the reference numeral 702 indicates theenlarged image. The convolution of a horizontal edge detection operator703 into the enlarged image generates an image 704. This is representedas h(x, y). On the other hand, the convolution of a vertical edgedetection operator 705 into the enlarged image generates an image 706.This is represented as v(x, y). h(x, y) represents thehorizontal-direction edge intensity, while v(x, y) represents thevertical-direction edge intensity. The edge direction dir(x, y) of eachpixel can be calculated by expression (3).

(MATH. 3)

dir(x,y)=tan⁻¹ {v(x,y)/h(x,y)}  (3)

FIG. 11( c) schematically represents the edge-direction calculationresult. The vertical edge is indicated by the solid line, the horizontaledge by the dashed line, and the oblique edge by the dotted line (inpractice, the edges are not limited to vertical/horizontal/oblique edgesbut each point has an edge direction represented by a real number).

S605: Receive the signal intensities (vertical and horizontal) of theedge portion determined in FIG. 9.S606: Give a gradation value to an edge point based on the edgedirection calculated in S604 and the signal intensity of the edgeportion (vertical direction: ev, horizontal direction eh) received inS605 (FIG. 11 (d)). More specifically, when the gradation value of anedge point is represented as edge(e, y), edge(e, y) is the length of avector with the direction of dir(x, y) on an ellipse with the verticaldiameter of ev and the horizontal diameter of eh. The simultaneousequation, represented by expressions (4) and (5), is solved for p and q,and the resulting p and q are substituted in expression (6) to find thegradation value edge(e, y).

[MATH. 4]

$\begin{matrix}{{\frac{p^{2}}{{eh}^{2}} + \frac{q^{2}}{{ev}^{2}}} = 1} & (4)\end{matrix}$[MATH. 5]

q/p=tan {dir(x,y)}  (5)

[MATH. 6]

edge(x,y)=√{square root over (p ² +q ²)}  (6)

S607: Receive the edge width w determined in FIG. 7.S608: Expand the edge width on the image to the width w.S609: Receive the signal intensity p of the pattern portion and thesignal intensity b of the background portion determined in FIG. 8.S610: Give the gradation value p and the gradation value b,respectively, to the pattern portion and the background portion on theimage (FIG. 11 (e)).S611: Receive the beam size (a of Gaussian function) calculated in FIG.9.S612: Convolute the Gaussian function into the image obtained in S610(FIG. 11( f).

The content of processing in step 526 in FIG. 1( b) or in step S208 inFIG. 3 is as described above. According to this embodiment, a simulatedSEM image, which is used for comparison with the real SEM image, can begenerated from the design data using the simulated SEM image generationparameters calculated in advance.

(1-7) Setting of Imaging Points of SEM Image for Calculating SimulatedSEM Image Generation Parameters

With reference to FIG. 13 to FIG. 15, the following describes steps S510and S511 in FIG. 1( a), that is, the setting method of the imagingpoints of a real SEM image used for calculating the values of thesimulated SEM image generation parameters.

FIG. 13 shows an example of the GUI screen for setting the imagingpoints of a SEM image for calculating the signal intensity p of apattern portion. When “Manual” is selected in “Set imaging point forcondition setting” 501, a chip map 502 is displayed, and design data 504corresponding to the cursor position (503) on the chip map is displayedon the screen. On the design data, wide pattern areas 506 and 507,suitable for calculating the pattern portion signal intensity, arespecified using a pointing device 505. The specified area is listed inan area list 508 as a candidate. After listing the areas, the whole or apart of the areas is specified (specified by the check mark), a chipnumber 509 is entered, and then it is saved as an imaging pointinformation file (510). This method allows an imaging point to bedetermined offline without imaging the real SEM image.

FIG. 14 shows another example of the GUI screen for setting the imagingpoints of a SEM image for calculating the signal intensity p of apattern portion. When “Auto” is selected in “Set imaging point forcondition setting” 520, a chip map 521 is displayed. On this map, asearch area is set with a pointing device or by entering a numeric value522. Design data 523 corresponding to the area that is set is displayedon the screen. Because a wide pattern area is suitable for calculatingthe signal intensity of the pattern portion, a pattern horizontal widthminimum value 524 and a pattern vertical width minimum value 525 areentered and an “Apply” 526 is clicked. Then, a pattern, which isincluded in the search area specified by the value 522 and whosevertical width and horizontal width are larger than those specified asthe values 524 and 525, is automatically selected, is displayed on thedesign data on the screen (527, 528), and is listed in an area list 529as a candidate. After listing the areas, the whole or a part of theareas is specified (specified by the check mark), a chip number 530 isentered, and then it is saved as an imaging point information file(531). To reliably find the signal intensity of the pattern portion, itis advantageous to specify more areas for calculating the averagebrightness of the areas. To set more areas, the method shown in FIG. 14is more convenient than the method shown in FIG. 13.

FIG. 15 shows an example of the GUI screen for setting the imagingpoints of a SEM image for calculating the signal intensity of an edgeportion. The screen configuration is similar to that in FIG. 11 exceptfor a pattern search condition 540. While the minimum values of thevertical and horizontal widths of the pattern are entered in FIG. 11, apattern width minimum value 541, a pattern interval minimum value 542,and a continuous edge length minimum value 543 are entered on thisscreen. This is because the wider the pattern width and the patterninterval and the longer the continuous edge length, the more reliablycan the parameters, described in (1-5), be calculated. As in FIG. 14,the selected areas are listed in the area list.

(1-8) Imaging of SEM Image for Calculating Simulated SEM ImageGeneration Parameters

With reference to FIG. 16, the following describes steps S512 and S513in FIG. 1( a); that is, the following describes a sequence of processingin which the real SEM image for calculating the simulated SEM imagegeneration parameters is imaged, the parameters are calculated usingthis real SEM image, and the result is saved.

FIG. 16 shows an example of the GUI screen for executing the sequencedescribed above. First, an imaging condition is received (S550). In thisstep, the following are received: accelerating voltage, imagingmagnification, beam current, whether the image is acquired by scanningthe stage or acquired with the stage stopped, stage movement directionwith respect to wafer, and beam scanning angle. The received content iswritten in the model condition file. Because a change in these imagingconditions involves a change in the image quality of the SEM image, itis necessary that the simulated SEM image generation parameters belinked to the imaging conditions and that the imaging condition atinspection time (FIG. 1( b)) be the same as the imaging conditionreceived in S550. At inspection time, the model condition file is readand the same imaging condition is applied.

Next, the brightness correction coefficient is determined (S551). Thebrightness correction coefficient is a coefficient used for adjustingthe gain and the offset for the output of the detector (108 in FIG. 1)so that the brightness of the real SEM image becomes proper. When thesignal intensity before brightness correction is represented by i, thesignal intensity after brightness correction is represented by j, thegain adjustment coefficient is represented by “gain”, and the offsetadjustment coefficient is represented by “offset”, the followingrelation holds.

(MATH. 7)

j=gain×i+offset  (7)

When a “Perform imaging” button 552 is clicked, imaging is performed ina suitable area in which both the pattern and the background areincluded. When “Calculate brightness correction coefficient” button 553is clicked, the maximum value and the minimum value of the imagebrightness are calculated and “gain” and “offset” are calculated so thatthe maximum and minimum values become proper values. The calculationresult of the brightness correction coefficient is written in the modelcondition file. Because a change in the brightness correctioncoefficient involves a change in the brightness of the SEM image, it isnecessary that the simulated SEM image generation parameters be linkedto the brightness correction coefficient and that the brightnesscorrection coefficient at inspection time (FIG. 1( b)) be the same asthe result calculated in S553. At inspection time, the model conditionfile is read and the same brightness correction coefficient is applied.

Next, the pattern portion signal intensity, one of the simulated SEMimage generation parameters, is calculated (S554). Because the imagingpoint on the real SEM image for calculating the pattern portion signalintensity is already determined in step S511 in FIG. 1, the imagingpoint information file, in which the imaging point information isdescribed, is specified in an area 555. When a “Perform imaging” button556 is clicked, the real SEM image at the coordinates, described in theimaging point information file, is acquired under the imaging conditionreceived in S550 and at the brightness coefficient determined in SS551.After that, when a “Calculate pattern signal intensity” button 557 isclicked, the pattern signal intensity (signal intensity of p indicatedby the signal waveform 310 in FIG. 6) is calculated using the methoddescribed in FIG. 8. The calculation result is written in the modelcondition file.

The other simulated SEM image generation parameters, not shown, arecalculated in the same manner, and these parameters are written also inthe model condition file.

Finally, the condition file is saved (S558). When a “Save conditionfile” button 559 is clicked, the model condition file is saved with adesired file name. When a “Save image” button 560 is clicked, the imageused in step S551 or step S554 is saved.

(1-9) Effect of the First Embodiment

According to the first embodiment described above, the inspection can beperformed by comparing a real SEM image and a simulated SEM image.Although defects that are generated in any of all dies cannot bedetected in the conventional D2D inspection, these defects can bedetected in this embodiment. In addition, to generate a simulated SEMimage, the parameters for generating the simulated SEM image must bedetermined accurately. To meet this requirement, this embodimentprovides a specific parameter calculation method. In addition, thisembodiment provides the parameter calculation support function that usesdesign data. This function allows the user of the inspection device tocalculate the parameters easily and accurately. This generates asimulated SEM image still closer to the real SEM image, thus providinghigh inspection performance.

Second embodiment

A second embodiment is an embodiment in which other types of design dataare used. FIG. 17 shows the variations of design data. FIG. 17( a) showsa design intent, (b) shows a mask pattern, (c) shows a lithographysimulation result with a mask pattern as its input, and (d) shows acontour line based on a lithography simulation result.

Although not specifically mentioned, (d) is used in the firstembodiment. Of the remaining (a), (b), and (c), (c) representsmultivalued data. The amount of multivalued data is so large that fewinspection device users save the data of the whole inspection area inthis format. Therefore, in the second embodiment, the case in which (a)is used and the case in which (b) is used are described. (d) has a shapesimilar to that of a real pattern on a wafer, while the deviation of (a)and (b) from a real pattern is so large that the preprocessing isrequired in each case to convert the shape.

First, FIG. 18 shows the preprocessing that is performed when a designintent (FIG. 17( a)) is used. As indicated by the reference numeral 901,a design intent is generally configured by straight lines and has90-degree corners. Because a corner of a pattern on the real wafer isround, the rounding of the corner is required as the preprocessing. Theprocessing is performed in which the corners are detected from theintent design data (S902), a plurality of patterns (903-905) each havinga different rounding intensity level are generated, and the roundingintensity is optimized by matching between each of the generatedpatterns and the real pattern. More specifically, the pattern with themaximum correlation coefficient between the real SEM image and theprocessed image is selected or the pattern is determined through visualobservation. The rounding processing may be performed either at a timebefore the inspection or in synchronization with the inspection.

Next, FIG. 19 shows the preprocessing that is performed when a maskpattern (FIG. 17( b)) is used. OPC (optical proximity correction) hasbeen performed on the mask pattern, and the preprocessing, whichsimulates exposure simulation, must be performed. First, a multivaluedimage 911, which has brightness gradient on the edge portion, isgenerated by convoluting a blur function 910 (such as Gaussian function)into the mask pattern. After that, a plurality of binarized patterns(912-914) are generated by varying the slice level, and the twoparameters, a blur filter size 910 and the slice level, are optimized sothat they match the real pattern. As with the rounding processing, thepattern with the maximum correlation coefficient between the real SEMimage and the processed image is selected or the pattern is determinedthrough visual observation (915).

FIG. 20 shows a simulated SEM image generation flow in the secondembodiment that corresponds to the simulated SEM image generation flowin the first embodiment shown in FIG. 10. With the design data (maskpattern) and the simplified exposure simulation parameters (blur filtersize, slice level), determined as described above, as the input (S920,S921), simplified exposure simulation, that is, binarization based onblur filter convolution and specified slice levels, is performed (922).The subsequent processing is similar to that shown in FIG. 10.

Because exposure simulation generally requires a huge amount of time,the inspection device user has not always performed exposure simulationfor the whole die area. According to the second embodiment describedabove, the applicable range is expanded in the sense that the D2DBinspection can be implemented when the design data owned by theinspection device user is any one of a design intent or a mask pattern.In addition, when comparing the data amount among various types ofdesign data, the more the number of vertices of a design pattern is, thelarger the data amount is. Therefore, the data amount is in the order ofdesign intent<mask pattern<lithography simulation. This embodiment usessmaller-amount design data, thus providing the advantage in the timerequired for data transfer and the memory capacity required for storingdata.

Third Embodiment

FIG. 21 shows a third embodiment. The flow in FIG. 21 is similar to theflow in FIG. 3 in the first embodiment except that there is a pluralityof simulated SEM images.

The reading of design data (S230), the reading of a SEM image (S231),the detection of a positional deviation (S232), and distortioncorrection and extraction (S233) are the same as those in the firstembodiment. When generating a simulated SEM image in step S234, aplurality of simulated SEM images, different with each other in thepattern width (horizontal axis in the figure) and in the edge width(vertical axis in the figure, w in FIG. 7( b)), is generated. The imagecomparison (S235) is performed with the plurality of simulated SEMimages and, only when a defect is detected in all comparisons, thedefect information is output. In other words, if no defect is detectedin at least one comparison, no defect information is output.

This method allows for a variation in the semiconductor process. Thatis, the purpose is to reduce an error in the inspection accuracy thatmay be caused by a variation in the semiconductor process. For example,when the exposure amount of the exposure device is increased ordecreased in the semiconductor exposure process, the pattern width isincreased or decreased. Similarly, when the focus of the exposure devicevaries, the edge width is increased or decreased. When the semiconductorprocess state differs between the wafer used for calculating theparameters for generating a simulated SEM image and the wafer to beinspected, the pattern width or the edge width differs between thesimulated SEM image and the real SEM image, resulting in a defect in thewhole area. This result, though correct if a change in the line width orthe edge width is detected as a defect, is inconvenient when detecting adecrease or an increase in a local pattern. The purpose of thisembodiment is to avoid this problem.

According to this embodiment, a pattern width change in the whole areaor a gently-rising edge in the whole area, which may be caused by aprocess variation, is not detected as a defect, but only a local shapechange can be detected.

Although the pattern width and the edge width are used as parameters forgenerating a plurality of simulated SEM images in the above description,only one of them may be used or another shape parameter (rounding of acorner, a recession in a pattern tip, etc.) may be changed. It isdesirable for the user to set the parameter variation range before theinspection in the sense that the upper limit of the allowance amount ofa process variation is set.

Fourth Embodiment

FIG. 22 shows a fourth embodiment. As with the third embodiment, thepurpose of the fourth embodiment is to allow for a process variation.

In this embodiment, a plurality of simulated SEM images is not generatedin step S240 but one type of simulated SEM image is used in thecomparison for defect determination (S241, S242). For a portiondetermined as defective, the design data corresponding to the real SEMimage is saved and is compared with a plurality of simulated SEM imagesin the defect re-determination processing (S243). The defectre-determination processing may be performed either in synchronizationwith the inspection or later using the saved data.

This embodiment, in which the inspection sensitivity can be freelychanged later, is convenient when it is difficult to set the allowanceamount of a process variation in advance. This embodiment is alsoconvenient for analyzing a change in the occurrence of a defect when theallowance amount of a process variation is changed.

Fifth Embodiment

FIG. 23 shows a fifth embodiment. A resist pattern is inspected in thefirst embodiment as shown in FIG. 6, while a non-resist pattern isinspected in the fifth embodiment.

FIG. 23( a) shows an inspection in which a silicon pattern is inspected,and FIG. 23( b) shows an inspection in which a silicon trench isinspected. In this case, too, the modeling method is basically similarto that in FIG. 6, with a SEM image represented by three types of signalintensity, i.e., the signal intensity of the pattern portion, the signalintensity of the edge portion, and the signal intensity of thebackground portion. FIG. 23( c) shows a case in which a pattern isincluded in the bottom layer. Because there are two types of signalintensity for the background in this case, the SEM image is representedby a total of four types of signal intensity. For calculating thesimulated SEM image generation parameters, the same method as that inthe first embodiment can be used.

According to this embodiment, the inspection method is applicable notonly to a resist pattern but also to the systematic defect inspection ofthe processes of semiconductor patterning such as a gate pattern, awiring pattern, and an STI pattern.

Sixth Embodiment

FIG. 24 shows a sixth embodiment. In this embodiment, modeling isperformed in more detail than in the modeling of a SEM image (FIG. 6) inthe first embodiment. FIG. 24( a) is a diagram showing the simulation ofthe SEM image of a resist pattern. When the wavelength of an exposurelight for patterning a resist becomes smaller than the pattern width ofthe resist, the taper angle of the pattern edge becomes varied due tothe optical proximity effect. FIG. 24( a) shows the edge portion as alight zone. As shown in the figure, a high pattern density tends tocause the edge to rise perpendicularly (narrower edge width in theimage), and a low pattern density tends to cause the edge to rise gently(wider edge width in the image), in many cases. In this embodiment,design data 950 is used as shown in FIG. 24( b) and, for each point ofinterest, the distance to the neighboring pattern is calculated (theresult is that the distance indicated by the reference numeral 952 islonger than the distance indicated by the reference numeral 951). Thelookup table (FIG. 24( c)), which represents the relation between thedistance to the neighboring pattern and the edge width, is referenced tofind the edge width.

In the first embodiment, modeling is simple because all edge widths areequal as shown in FIG. 7. In the sixth embodiment, the edge width variesaccording to the distance to the neighboring pattern. When the imagingmagnification of an image is low, there is no practical problem with thesimple modeling in which the edge width is a fixed value. However, whenthe imaging magnification is high, it is desirable to apply thisembodiment for high-accuracy inspection.

Seventh Embodiment

FIG. 25 shows an overall flow in a seventh embodiment. In the firstembodiment (FIG. 1), the parameters for generating a simulated SEM imageare determined before the inspection. In the seventh embodiment, theimage of an inspection area is imaged first and then saved and, later,the saved image and the design data are compared for inspection.

FIG. 25( a) shows a flow of the imaging of an image. First, the designdata is read (S801) and an inspection area is specified on the designdata (S802). The SEM image of the specified area is imaged (S803), andthe imaged image is saved (S804). As in step S523 in FIG. 1, apositional deviation is detected (S805), a positional deviation map(similar to the one indicated by the reference numeral 206 in FIG. 3) iscalculated, and the calculated positional deviation map is saved (S806).

FIG. 25( b) shows a flow of inspection performed through comparisonbetween the imaged image and the design data. Because imaging is alreadycompleted, the subsequent flow may be performed on the inspection deviceor on a computer installed outside the inspection device.

First, the saved SEM image, positional deviation map, and design dataare read (S811, S812, S813). After that, the parameters for generatingthe simulated SEM image are calculated using the imaged SEM image(S814). The method is similar to that in step S513 in the firstembodiment. The simulated SEM image is generated using the calculatedparameters (S815) and is compared with the inspection image (S816) fordetermining a defect and for calculating the characteristic amount ofthe defective portion (S817).

This embodiment, in which parameter setting is performed using theinspection image to be used in the actual inspection, is moreadvantageous than the first embodiment from the viewpoint of parameteraccuracy. However, when the inspection area is large (for example, wheninspecting the whole area of one die), it is difficult to save allimages. Therefore, this embodiment is efficient when the inspection areais small. It is also possible to store all images of a defective portionby performing the inspection according to the flow in FIG. 1 and, forthat image, to perform re-inspection according to the flow in FIG. 25(b). In this case, defect candidate areas are calculated according to theflow in FIG. 1, and an area is selected from the defect candidate areasaccording to the flow in FIG. 25( b).

Eighth Embodiment

FIG. 26 shows a flow in an eighth embodiment. The eighth embodiment issimilar to the first embodiment except that step 580 of brightnesscorrection is added to the inspection flow in the first embodiment (FIG.1( b)).

The condition setting, which is performed before the inspection, is thesame as that in the first embodiment shown in FIG. 1( a). In the firstembodiment, the simulated SEM image, generated by applying the simulatedSEM image generation parameters determined in the flow shown in FIG. 1(a), is used directly in comparison inspection (step S526 in FIG. 1). Onthe other hand, in the eighth embodiment, comparison inspection (S581)is performed after the brightness of the whole image is corrected(S580).

FIG. 27 shows a defect determination flow in this embodiment thatcorresponds to the defect determination flow (FIG. 3) in the firstembodiment. The steps for the reading of design data (S590), reading ofa SEM image (S591), detection of a positional deviation (S592),correction and extraction of a distortion (S593), and generation of asimulated SEM image (5594) are the same as those in the firstembodiment.

After these steps, the brightness correction coefficient is calculated(S595). Let the real SEM image be f(x, y), and let the simulated SEMimage be g(x, y). The coefficients a and b are determined in such a waythat the sum of squares (expression (8)) of the difference between theimage, generated by multiplying the simulated SEM image by a and thenadding b to the result, and the real SEM image is minimized (598).

(MATH. 8)

Σ{(a×g(x,b)−f(x,y)}²  (Expression 8)

After that, the simulated SEM image is converted to find g′(x, y) usingexpression (9). Image comparison is performed between the convertedsimulated SEM image g′(x, y) and the real SEM image f(x, y).

(MATH. 9)

g′(x,y)=a×g(x,y)+b  (Expression 9)

The bright correction (S596), performed as described above, produces thefollowing result. Before the correction, there is a brightness deviationbetween f(x, y) and g(x, y) as in the distribution diagram indicated bythe reference numeral 599 (the points are plotted in the diagram wherethe horizontal axis indicates the brightness of the coordinates (x, y)of f(x, y) and the vertical axis indicates the brightness of thecorresponding coordinates of g(x, y)). After the correction, thedistribution diagram changes to the one, indicated by the referencenumeral 600, where the points distributed around the line y=x.

In some cases, the device state changes between the time when theparameters for simulated SEM image generation are calculated and thetime when the inspection is made, with the result that the stateindicated by the reference numeral 599 is generated. In this case, theimage comparison (S597) between f(x, y) and g(x, y) using expression (1)will generate an incorrect result in many cases. This embodiment, inwhich the brightness correction is performed, avoids this problem. Thecorrection coefficients, which are calculated in such a way that thebrightness match is achieved, not a local brightness basis, but on awhole image brightness basis, prevent a defective portion from beingoverlooked.

The conversion processing, though performed for the simulated SEM imagein the description above, may be performed for the real SEM image.

In addition to the case in which the device state changes as describedabove, this embodiment is applicable also to the case in which thebrightness changes on a whole image basis because the wafer, used forcalculating the parameters for generating the simulated SEM image, andthe wafer to be inspected are different.

Ninth Embodiment

The ninth embodiment is an additional function of the eighth embodiment.In the eighth embodiment, the brightness correction coefficient iscalculated for each image. On the other hand, in this embodiment, thepast brightness correction coefficients are referenced to calculate thecurrent brightness correction coefficient in order to calculate thebrightness correction coefficient more reliably.

FIG. 28 is a diagram in which the points are plotted with the time t onthe horizontal axis and the brightness correction coefficient a on thevertical axis. Because the patterns included in the acquired imagesdiffer with each other, the brightness correction coefficient ‘a’calculated for each image includes an error to some extent with theresult that the plotted points are distributed as indicated by thereference numeral 850. In this embodiment, the brightness coefficientA(t) to be used for the current time t is determined by (expression 10)using the past brightness correction coefficients, where a(t) is thebrightness correction coefficient at the current time t.

(MATH. 10)

$\begin{matrix}\begin{matrix}{{A(t)} = {\left( {{a(t)} + {A\left( {t - 1} \right)} + {A\left( {t - 2} \right)}} \right)\text{/}3}} \\{= \left\{ {{a(t)} + {\left( {{a\left( {t - 1} \right)} + {a\left( {t - 2} \right)} + {a\left( {t - 3} \right)}} \right)\text{/}3} +} \right.} \\{\left. {\left( {{a\left( {t - 2} \right)} + {a\left( {t - 3} \right)} + {a\left( {t - 4} \right)}} \right)\text{/}3} \right\} \text{/}3}\end{matrix} & \left( {{Expression}\mspace{14mu} 10} \right)\end{matrix}$

A(t) obtained by this calculation is a smooth curve as indicated by thereference numeral 851. Using A(t) instead of a(t) allows the brightnesscorrection coefficient to be calculated more reliably, resulting in anincrease in the accuracy of the inspection. For example, when an imageincludes a large defect, the brightness correction coefficientcalculated in the eighth embodiment is affected by the large defect. Inthis case, the ninth embodiment, if used, reduces this problem.

The brightness correction coefficient may be calculated by simply using(expression 10), or irregular data may be deleted from the calculationby detecting an irregularity in the correction coefficients. Althoughthe brightness correction coefficient a is described as an exampleabove, the same processing may of course be performed for b.

Tenth Embodiment

FIG. 29 shows a flow in a tenth embodiment. In the first embodiment, aninspection is made for a relatively large area for locating a defect; onthe other hand, in the tenth embodiment, a fixed-point inspection ismade for inspecting only a specified area.

The condition setting, which is performed before the inspection, is thesame as that in the first embodiment (FIG. 1( a)). FIG. 29 shows a flowcorresponding to the flow in FIG. 1( b) in the first embodiment.

First, the model condition file and the design data are read (S880,S881) and a position, where a fixed-point inspection is to be made, isspecified on the design data (S882). The position, where a fixed-pointinspection is to be made, is a point determined by the processsimulation as a possible defective position or a point where a defectwas frequently generated in the past inspections. Next, the real SEMimage of the specified position is acquired (S883), a positionaldeviation between the design data and the real SEM image is detected(S884), and the design data is transformed and extracted based on thepositional deviation detection result (S885). After that, as in thefirst embodiment, the parameters described in the model condition filethat is read in step S880 are applied to generate a simulated SEM image(S886). In the next step S887 where brightness correction is performed,the method described in the eighth embodiment is applied. After that,the inspection is performed by comparing the simulated SEM image, forwhich brightness correction has been made, with the real SEM image(S888).

FIG. 30 shows an output example of a fixed-point inspection. FIG. 30(a), a schematic diagram of a real SEM image, shows a line pattern thathas a concave defect (partially thinned line) in an intermediateposition. The difference between this image and the simulated SEM imageis as shown in FIG. 17( b). As shown in the figure, the brightness inthe difference image differs according to whether the brightness of thereal SEM image is higher than that of the simulated SEM image or thebrightness of the real SEM image is lower than that of the simulated SEMimage.

The output of a defect includes the defect type (concave defect orconvex defect) determined according to whether the defective portion isinside or outside of the design pattern, the vertical and horizontalsizes of the defect, the area of the defect, or the brightnessdifference in the defective portion.

According to this embodiment, a comparison with a simulated SEM imagecan be performed in a fixed-point inspection. By comparing with asimulated SEM image, a quantitative evaluation can be made not only fora defect occurring as a difference in the shape as described above butalso for a defect occurring as a difference in the brightness such as athin-film remnant.

Eleventh Embodiment

An eleventh embodiment relates to the setting of a threshold for defectdetermination. In the first embodiment, an image is determined asdefective if the shade difference between a real SEM image and asimulated SEM image is equal to or larger than a fixed value as shown in(MATH. 1); on the other hand, in this embodiment, a threshold is setaccording to the brightness g(x, y) of a simulated SEM image.

The characteristic of a SEM image is that the brighter the image is, thehigher the noise is (characteristic of a shot noise). Therefore, anincorrect determination or a defect-detection failure can be reduced bysetting a smaller defect-determination threshold for a dark portion, anda larger defect-determination threshold for a bright portion.

In this embodiment, a lookup table, which represents the relationbetween the brightness of the simulated SEM image and defectdetermination thresholds, is created before the inspection as shown inFIG. 31. At inspection time, this lookup table is referenced for defectdetermination.

The point here is that the brightness of a simulated SEM image is usedto reference the lookup table. Instead, if the brightness of a real SEMimage is used, the brightness of a defective portion is used toreference the lookup table for a defective portion, possibly preventingthe intended purpose of performing an inspection using noisecharacteristics from being achieved. In this embodiment, the brightnessof the simulated SEM image is used to reference the lookup table toachieve the intended purpose.

This embodiment enables a high-accuracy inspection with the effect of aSEM noise minimized.

Twelfth Embodiment

A twelfth embodiment relates to positional deviation detection.

In the first embodiment, the design data and the real SEM image are usedto detect a positional deviation (S205) as shown in FIG. 3. In thetwelfth embodiment, the design data is read (S1001), a simulated SEMimage is generated from this design data (S1003, the parameters forgenerating the simulated SEM image are read from the model conditionfile not shown), and a positional deviation detection is performed(S1004) between the generated simulated SEM image and the real SEM imagethat is read (S1002) as shown in FIG. 32. The subsequent processing issimilar to that in the first embodiment.

In general, the accuracy of positional deviation detection is increasedby performing positional deviation detection between the images withsimilar characteristics.

This embodiment increases the accuracy of positional deviation detectionand increases the accuracy of distortion correction and extraction inthe subsequent stages, resulting in an increase in inspectionperformance.

Thirteenth Embodiment

A thirteenth embodiment relates to the acquisition of a real SEM image.In the description of the SEM image acquisition method in the firstembodiment, one of the following two types of images, continuous imageand a sheet image, is inspected. A continuous image is an image obtainedby the one-dimensional scanning of an electron beam and the continuousmovement of the stage, and a sheet image is an image obtained by thetwo-dimensional scanning of an electron beam and the step movement ofthe stage. As shown in FIG. 3, the stage movement direction and the beamscanning direction may be tilted.

FIG. 33( a) shows a method in which the stage is moved in parallel tothe chip layout on a wafer and the electron beam scanning is performedobliquely with respect to the chip layout on a wafer.

FIG. 33( b) shows a method in which both the horizontal scanningdirection and the vertical scanning direction of the electron beam aretilted with respect to the chip layout on a wafer.

Most of semiconductor patterns are configured by patterns at rightangles to, or parallel to, the chip layout. In an electron beam image,an edge parallel to the scanning direction of the electron beam tends toget blurred due to electrification as shown in FIG. 5. In thisembodiment, because the scanning direction of the electron beam and theedge direction of a pattern are not parallel in many cases, the numberof blurred edges is decreased.

When a real SEM image is acquired via oblique scanning, the simulatedSEM image must also be transformed to produce a similar image. To do so,because the oblique scanning angle is known (this angle is entered viathe GUI shown in FIG. 16), the simulated SEM image is transformed usingthe oblique scanning angle in the step of transformation and extractionof design data (S523 in FIG. 1).

According to this embodiment, it is expected that fewer edges willbecome blurred and therefore the inspection sensitivity will beincreased.

Fourteenth Embodiment

A fourteenth embodiment relates to the electronic optical system.

In the first embodiment, a single-beam optical system is used as shownin FIG. 2, On the other hand, in this embodiment, a multi-beam opticalsystem is used as shown in FIG. 34.

Because there may be individual differences among a plurality of emittedelectron beams and a plurality of detectors in the multi-beam system,the parameters for generating a simulated SEM image are calculated foreach beam. In addition, it is desirable that the brightness correctiondescribed in the eighth embodiment be performed for each beam.

In this embodiment, it is expected that the inspection throughput willbe increased.

The fourteen embodiments have been described. It is also possible tocombine these embodiments in part or in whole.

1. A pattern inspection device comprising: an imaging unit that imagesan electron beam image of a pattern formed on a substrate; a simulatedelectron beam image generation unit that generates a simulated electronbeam image using a parameter based on design data, the parameterrepresenting a characteristic of the electron beam image; and aninspection unit that inspects the pattern on the substrate by comparingthe electron beam image of the pattern and the simulated electron beamimage, the electron beam image of the pattern being imaged by saidimaging unit, the simulated electron beam image being generated by saidsimulated electron beam image generation unit.
 2. A pattern inspectiondevice comprising: an imaging unit that images an electron beam image ofa pattern formed on a substrate; a pattern shape transformation unitthat transforms a shape of the pattern using a parameter based on designdata, the parameter representing a processing characteristic of thepattern; a simulated electron beam image generation unit that generatesa simulated electron beam image for the pattern using a parameter, thepattern being transformed by said pattern shape transformation unit, theparameter representing a characteristic of the electron beam image; andan inspection unit that inspects the pattern on the substrate bycomparing the electron beam image of the pattern and the simulatedelectron beam image, the electron beam image of the pattern being imagedby said imaging unit, the simulated electron beam image being generatedby said simulated electron beam image generation unit.
 3. The patterninspection device according to claim 1, further comprising: a conditioninput unit that automatically determines a parameter value, necessaryfor generating the simulated electron beam image, from the design data;and a parameter calculation unit.
 4. The pattern inspection deviceaccording to claim 1 wherein the parameter representing thecharacteristic of the electron beam image, generated by said simulatedelectron beam image generation unit, include at least one of abrightness of a pattern portion, a brightness of a background portion, abrightness of an edge portion for each direction, and a blur amount ofthe edge portion.
 5. The pattern inspection device according to claim 2,further comprising: a variation range setting unit that sets a variationrange for a parameter representing an exposure characteristic; and atransformation pattern generation unit that generates a plurality oftransformation patterns each corresponding to the variation range thatis set by said variation range setting unit, wherein said simulatedelectron beam image generation unit generates a plurality of simulatedelectron beam images corresponding to the plurality of transformationpatterns generated by said transformation pattern generation unit, andsaid inspection unit produces a plurality of inspection results linkedto the parameter representing the exposure characteristic by comparingthe electron beam image of the pattern and the plurality of simulatedelectron beam images, the electron beam image of the pattern beingimaged by said imaging unit, the plurality of simulated electron beamimages being generated by said simulated electron beam image generationunit.
 6. The pattern inspection device according to claim 1, furthercomprising: a variation range setting unit that sets a variation rangefor the parameter representing the characteristic of the electron beamimage wherein said simulated electron beam image generation unitgenerates a plurality of simulated electron beam images, correspondingto the variation range, for the parameter representing thecharacteristic of the electron beam image that is set by said variationrange setting unit, and said inspection unit produces a plurality ofinspection results linked to the parameter representing thecharacteristic of the electron beam image by comparing the electron beamimage of the pattern and the plurality of simulated electron beamimages, the electron beam image of the pattern being imaged by saidimaging unit, the plurality of simulated electron beam images beinggenerated by said simulated electron beam image generation unit.
 7. Thepattern inspection device according to claim 5 wherein said variationrange setting unit updates a value of the parameter representing thecharacteristic of the electron beam image based on a temporal transitionof the plurality of inspection results that are linked to the parameterrepresenting the characteristic of the electron beam image.
 8. Thepattern inspection device according to claim 1, further comprising: abrightness adjustment unit that adjusts brightness between the electronbeam image of the pattern and the simulated electron beam image; and abrightness adjustment parameter storage unit that stores a brightnessadjustment parameter between the electron beam image of the pattern andthe simulated electron beam image.
 9. The pattern inspection deviceaccording to claim 8 wherein said brightness adjustment parameterstorage unit updates a value of the brightness adjustment parameterbased on a temporal transition of the stored brightness adjustmentparameter.
 10. A pattern inspection method comprising: an imaging stepfor imaging an electron beam image of a pattern formed on a substrate; asimulated electron beam image generation step for generating a simulatedelectron beam image using a parameter based on design data, theparameter representing a characteristic of the electron beam image; andan inspection step for inspecting the pattern on the substrate bycomparing the electron beam image of the pattern and the simulatedelectron beam image, the electron beam image of the pattern being imagedby said imaging step, the simulated electron beam image being generatedby said simulated electron beam image generation step.
 11. A patterninspection method comprising: an imaging step for imaging an electronbeam image of a pattern formed on a substrate; a pattern shapetransformation step for transforming a shape of the pattern using aparameter based on design data, the parameter representing a processingcharacteristic of the pattern; a simulated electron beam imagegeneration step for generating a simulated electron beam image for thepattern using a parameter, the pattern being transformed by said patternshape transformation step, the parameter representing a characteristicof the electron beam image; and an inspection step for inspecting thepattern on the substrate by comparing the electron beam image of thepattern and the simulated electron beam image, the electron beam imageof the pattern being imaged by said imaging step, the simulated electronbeam image being generated by said simulated electron beam imagegeneration step.
 12. The pattern inspection method according to claim10, further comprising: a condition input step for automaticallydetermining a parameter value, necessary for generating the simulatedelectron beam image, from the design data; and a parameter calculationstep.
 13. The pattern inspection method according to claim 10 whereinthe parameter representing the characteristic of the electron beamimage, generated by said simulated electron beam image generation step,include at least one of a brightness of a pattern portion, a brightnessof a background portion, a brightness of an edge portion for eachdirection, and a blur amount of the edge portion.
 14. The patterninspection method according to claim 11, further comprising: a variationrange setting step for setting a variation range for a parameterrepresenting an exposure characteristic; and a transformation patterngeneration step for generating a plurality of transformation patternseach corresponding to the variation range that is set by said variationrange setting step, wherein said simulated electron beam imagegeneration step generates a plurality of simulated electron beam imagescorresponding to the plurality of transformation patterns generated bysaid transformation pattern generation step, and said inspection stepproduces a plurality of inspection results linked to the parameterrepresenting the exposure characteristic by comparing the electron beamimage of the pattern and the plurality of simulated electron beamimages, the electron beam image of the pattern being imaged by saidimaging step, the plurality of simulated electron beam images beinggenerated by said simulated electron beam image generation step.
 15. Thepattern inspection method according to claim 10, further comprising: avariation range setting step for setting a variation range for theparameter representing the characteristic of the electron beam imagewherein said simulated electron beam image generation step generates aplurality of simulated electron beam images, corresponding to thevariation range, for the parameter representing the characteristic ofthe electron beam image that is set by said variation range settingstep, and said inspection step produces a plurality of inspectionresults linked to the parameter representing the characteristic of theelectron beam image by comparing the electron beam image of the patternand the plurality of simulated electron beam images, the electron beamimage of the pattern being imaged by said imaging step, the plurality ofsimulated electron beam images being generated by said simulatedelectron beam image generation step.
 16. The pattern inspection methodaccording to claim 14 wherein said variation range setting step updatesa value of the parameter representing the characteristic of the electronbeam image based on a temporal transition of the plurality of inspectionresults that are linked to the parameter representing the characteristicof the electron beam image.
 17. The pattern inspection method accordingto claim 10, further comprising: a brightness adjustment step foradjusting brightness between the electron beam image of the pattern andthe simulated electron beam image; and a brightness adjustment parameterstorage step for storing a brightness adjustment parameter between theelectron beam image of the pattern and the simulated electron beamimage.
 18. The pattern inspection method according to claim 17 whereinsaid brightness adjustment parameter storage step updates a value of thebrightness adjustment parameter based on a temporal transition of thestored brightness adjustment parameter.